NonLinModelling: Chaotic Time Series Modelling

Description Usage Arguments Value Author(s) References See Also Examples

Description

A collection and description of functions to simulate different types of chaotic time series maps.

Chaotic Time Series Maps:

tentSim Simulates data from the Tent Map,
henonSim simulates data from the Henon Map,
ikedaSim simulates data from the Ikeda Map,
logisticSim simulates data from the Logistic Map,
lorentzSim simulates data from the Lorentz Map,
roesslerSim simulates data from the Roessler Map.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
tentSim(n = 1000, n.skip = 100, parms = c(a = 2), start = runif(1), 
    doplot = FALSE)
henonSim(n = 1000, n.skip = 100, parms = c(a = 1.4, b = 0.3), 
    start = runif(2), doplot = FALSE)
ikedaSim(n = 1000, n.skip = 100, parms = c(a = 0.4, b = 6.0, c = 0.9), 
    start = runif(2), doplot = FALSE)
logisticSim(n = 1000, n.skip = 100, parms = c(r = 4), start = runif(1), 
    doplot = FALSE)
lorentzSim(times = seq(0, 40, by = 0.01), parms = c(sigma = 16, r = 45.92, 
    b = 4), start = c(-14, -13, 47), doplot = TRUE, ...)
roesslerSim(times = seq(0, 100, by = 0.01), parms = c(a = 0.2, b = 0.2, c = 8.0),
    start = c(-1.894, -9.920, 0.0250), doplot = TRUE, ...) 

Arguments

doplot

a logical flag. Should a plot be displayed?

n, n.skip

[henonSim][ikedaSim][logisticSim] -
the number of chaotic time series points to be generated and the number of initial values to be skipped from the series.

parms

the named parameter vector characterizing the chaotic map.

start

the vector of start values to initiate the chaotic map.

times

[lorentzSim][roesslerSim] -
the sequence of time series points at which to generate the map.

...

arguments to be passed.

Value

[*Sim] -
All functions return invisible a vector of time series data.

Author(s)

Diethelm Wuertz for the Rmetrics R-port.

References

Brock, W.A., Dechert W.D., Sheinkman J.A. (1987); A Test of Independence Based on the Correlation Dimension, SSRI no. 8702, Department of Economics, University of Wisconsin, Madison.

Eckmann J.P., Oliffson Kamphorst S., Ruelle D. (1987), Recurrence plots of dynamical systems, Europhys. Letters 4, 973.

Hegger R., Kantz H., Schreiber T. (1999); Practical implementation of nonlinear time series methods: The TISEAN package, CHAOS 9, 413–435.

Kennel M.B., Brown R., Abarbanel H.D.I. (1992); Determining embedding dimension for phase-space reconstruction using a geometrical construction, Phys. Rev. A45, 3403.

Rosenstein M.T., Collins J.J., De Luca C.J. (1993); A practical method for calculating largest Lyapunov exponents from small data sets, Physica D 65, 117.

See Also

RandomInnovations.

Examples

1
2
3
4
## logisticSim -
   set.seed(4711)
   x = logisticSim(n = 100)  
   plot(x, main = "Logistic Map")            

Example output

Loading required package: timeDate
Loading required package: timeSeries
Loading required package: fBasics


Rmetrics Package fBasics
Analysing Markets and calculating Basic Statistics
Copyright (C) 2005-2014 Rmetrics Association Zurich
Educational Software for Financial Engineering and Computational Science
Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY.
https://www.rmetrics.org --- Mail to: info@rmetrics.org
Loading required package: fGarch

fNonlinear documentation built on Nov. 17, 2017, 7:40 a.m.